A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter
This work addresses the specific task of emotion GIF recommendation on social media, representing an incremental improvement in a shared competition setting.
The paper tackled the problem of recommending emotion GIF categories for unlabeled tweets by proposing a hybrid BERT and LightGBM model, achieving a Mean Average Precision @ 6 score of 0.5394 and placing 4th in the EmotionGIF 2020 challenge.
The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion. Given their expressiveness, GIFs offer a more nuanced and precise way to convey emotions. In this paper, we present our solution for the EmotionGIF 2020 challenge, the shared task of SocialNLP 2020. To recommend GIF categories for unlabeled tweets, we regarded this problem as a kind of matching tasks and proposed a learning to rank framework based on Bidirectional Encoder Representations from Transformer (BERT) and LightGBM. Our team won the 4th place with a Mean Average Precision @ 6 (MAP@6) score of 0.5394 on the round 1 leaderboard.